VoltDB and Millions of Operations Per Second … So?

I’ve been looking at VoltDB recently, and thinking about comparisons with Coherence (and CloudTran of course, although what follows is purely about Coherence).

The first thing that hits you about VoltDB is the huge performance numbers. For example, this piece about Node.js and VoltDB quotes 675,000 transactions/sec on 8 clients and 12 servers. As each transaction did three reads and one write, this amounted to 2.8 million data operations per second. Each of the 12 servers had 8 cores for a total of 96 server cores, which means the test ran at just under 30,000 data operations per server core per second.

When you talk about databases, there is an assumption of durability – you assume that the data is getting saved to permanent storage. However, VoltDB is an in-memory database, and in fact in the above piece, the headline test was done with “k-factor=0” – which means, no backups at all (and presumably no persistent storage, as disk or SSD wasn’t mentioned). In other words, this is a lot like a data cache – you send a request to the server, cleverly routed to the correct partition, do some calculations with the targetted value plus related data, then store the result. Which raises the question – if you do this sequence of operations in a data cache, do you get similar performance?

To test this out, we loaded up some reference data, then sent a request (in Coherence, an entry processor) which read the targetted value, read two items of reference data, and then stored a computed result. We only had 7 ropey old development boxes available, so we wrote the application so that both client and server applications ran in each JVM, with the client load spread equally across the servers.

The bottom line was that our Coherence rig was more that 1.5X faster than VoltDB *per core* – 44,000 data operations per second versus 29,000 – even though the Coherence test was running client and server on the same machine.

For good measure, we tried “k-factor=1” – i.e. one backup – and got 28,300 operations per second, which is very close to VoltDB with no backups.

You’ll have to forgive me for not publishing more detailed information, but we’d need to get Oracle’s permission to publish a ‘benchmark’ – and this wasn’t a proper benchmark, just a few hours of running through a few scenarios and pretty much straight out of the box config. However, if you do want more detail, you can run it yourself: I’ve attached the Eclipse project and scripts for server-side deployment here… CoherencePerformance.zip


the point of this article is not the numbers themselves, but to point out that a data grid like Coherence and an in-memory database are likely to give very similar performance when they are doing relatively simple operations because it’s all the rest of the systems that cost the time. In fact, it is uncanny how similar these numbers are to tests we did on another IMDG a few years ago on the same equipment.

In other words, VoltDB may be superfast compared to database systems that store information to disk, but not compared to data grids storing all information in memory.

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